Courses and Curriculum

This course provides an applied introduction to microeconomic analysis, with a focus on how economic models and data can be used to understand and inform decision-making in firms, households, and markets. Central to the course is the question: What determines prices, and how do agents respond to them?

We examine core models of consumer and firm behavior and explore how those models can be used to analyze real-world economic activity. Topics include demand and supply, production and cost, market structure and pricing, decision-making under uncertainty and over time, and the role of information and incentives. The course also considers the implications of market failures—such as externalities, public goods, and imperfect competition—for economic efficiency and policy intervention.

Designed for students interested in applying economics tools in industry, consulting, technology, finance, or the public sector, the course emphasizes both conceptual foundations and empirical relevance. Students will learn how to use microeconomic reasoning and data to address practical questions related to market strategy, pricing, resource allocation, and policy evaluation.

In today’s data-driven economy, professionals across industries must understand how to interpret macroeconomic trends, evaluate the health of economies, and respond strategically to changes in growth, employment, inflation, and inequality. This course equips students with the tools to critically assess macroeconomic analyses from central banks, international institutions, and private-sector research teams.

We focus on how to work with real-world economic data—especially national income and product accounts (NIPA) data—and how to interpret key indicators of economic performance. Students will gain practical experience extracting, visualizing, and evaluating macroeconomic data, as well as insights into real-time challenges such as measurement error and data revisions.

The course is centered on the essential macroeconomic models, with an emphasis on the neoclassical growth framework and its application to understanding long-run trends in output, consumption, investment, and labor markets. We further examine the dynamics of income and wealth distributions, both within and across countries, and how their patterns relate to structural changes and policy.

Designed for students preparing for careers in industry, consulting, finance, tech, or the public and nonprofit sectors, the course bridges theory and practice—teaching students to think like economists while solving real-world problems.

Applied economists today operate at the intersection of economics, statistics, and data science. This course equips students with foundational tools for empirical economic analysis framed through the lens of modern machine learning and predictive modeling. It prepares students for advanced MEDS coursework by blending core principles of probability, statistics, and econometrics with contemporary computational techniques.

Topics include simulation-based estimation and inference (including the bootstrap); linear and nonlinear regression; high dimensionality and regularization (e.g., LASSO, ridge); model selection and cross-validation; overfitting and bias-variance tradeoffs; and causal versus non-causal prediction. Students will analyze cross-sectional, time-series, and panel data using tools from traditional econometrics alongside methods from machine learning, such as tree-based models, ensemble learning, and neural networks.

Throughout, emphasis is placed on building, evaluating, and interpreting predictive models, with hands-on implementation in modern computing environments.

This course develops the computational and programming foundations essential for applied economics, data science, and analytics. Emphasizing reproducibility, efficiency, and scalability, students gain hands-on experience using both R and Python—the two most widely used languages in modern data work. It is structured around four key components:

1. Computational workflow and collaboration: Students learn modern practices for collaborative development, including IDEs (RStudio, VS Code), version control with Git/GitHub, cloud-based coding environments (e.g., Jupyter Notebooks, Quarto), documentation with Markdown and LaTeX, and reproducible research workflows.
2. Programming for data science: Core programming concepts are covered in both R and Python, including control flow, modular code design, piping and chaining (e.g., dplyr, pandas), object-oriented and functional programming, unit testing, debugging, and database interaction via SQL. Code quality and maintainability are emphasized throughout.
3. Data acquisition and visualization: Students learn to access, clean, and structure real-world data from files, APIs, and the web, with an emphasis on handling messy and high-dimensional data. Advanced visualization techniques are taught using ggplot2 (R) and seaborn/plotly (Python), with a focus on communicating results to both technical and non-technical audiences.
4. Numerical methods for applied modeling: The course introduces practical numerical tools for economic and data modeling, including numerical differentiation and integration, root-finding, simulation, and optimization. Applications are drawn from real-world economic problems and implemented in both R and Python using packages such as optim, nloptr, scipy.optimize, and numdifftools.

By the end of the course, students will be equipped with a cross-language computational toolkit and the workflow skills needed to contribute effectively in modern data-driven roles across tech, finance, consulting, and policy sectors.

This course builds on the foundations of Microeconomics I to explore advanced topics in market design, incentives, and labor economics—focusing on how economic theory and data can inform real-world decisions in business, government, and policy.

We examine how strategic interaction, incentives, and information shape the design of auctions, compensation systems, regulations, and insurance contracts. Students will learn how to analyze these mechanisms from both theoretical and applied perspectives, with attention to efficiency, fairness, and practical implementation.

The second half of the course focuses on labor markets, including models of labor supply and demand, wage determination, and labor market equilibrium. We also explore deeper topics such as compensating wage differentials, principal-agent problems, human capital investment, government labor regulation, migration, and discrimination.

Throughout, the emphasis is on understanding how economists think about real-world institutional design and workplace dynamics—and how those insights can be applied in sectors such as consulting, tech, HR, policy analysis, and beyond.

This course builds on Macroeconomics I to examine advanced topics in macroeconomic policy, global finance, and the behavior of firms and economies over the business cycle. It is designed for students interested in applying macroeconomic insights to careers in finance, consulting, international business, and public policy.

Topics include the design and impact of fiscal and monetary policy across the business cycle, with a focus on modern tools and frameworks such as New-Keynesian DSGE (Dynamic Stochastic General Equilibrium) models. We explore how firms make investment decisions under uncertainty—addressing issues like sunk costs, option values, and macro-financial linkages.

The course also addresses international dimensions of macroeconomics, including global business cycle synchronization, trade and capital flows, exchange rate dynamics, and the causes and consequences of sovereign debt crises. Special attention is given to the role of international institutions such as the International Monetary Fund (IMF), the World Bank, the Bank for International Settlements (BIS), and the World Trade Organization (WTO) in shaping global economic stability and governance.

Throughout the course, students will engage with current research, policy debates, and data-driven applications to develop a deeper understanding of how macroeconomics informs decision-making at the firm, national, and international levels.

This course builds on Econometrics I to develop advanced tools for estimating and interpreting causal relationships—critical for data-driven decision-making in business, government, and the nonprofit sector.

A central focus is the problem of endogeneity, which arises when explanatory variables are correlated with unobserved factors, leading to biased estimates. The course explores why endogeneity occurs and how it can be addressed through methods such as instrumental variables, logistic and multinomial regression, panel data techniques, and structural vector autoregressions (SVARs).

Emphasis is placed on practical implementation using real-world datasets. Students will gain hands-on experience applying advanced econometric methods to real problems, such as evaluating policy interventions, analyzing marketing effectiveness, and modeling consumer and firm behavior in complex environments.

Designed for students pursuing careers in data science, analytics, consulting, policy analysis, and applied research, the course teaches not just how to estimate models—but how to think critically about causality, identification, and interpretation in real-world contexts.

Machine learning has become an essential tool across industries—from tech and finance to healthcare, retail, and public policy. This course provides an applied introduction to both supervised and unsupervised machine learning techniques, with a focus on how these tools can be used to uncover patterns, make predictions, and inform decisions in real-world settings.

Students will explore a range of methods, including dimensionality reduction (e.g., principal components, common factors, autoencoders), clustering techniques, and a variety of regularized regression approaches such as ridge regression and LASSO. The course also covers model selection and evaluation using techniques like sample splitting and model averaging.

We delve into both traditional nonparametric methods (such as kernel regression and nearest neighbors) and modern machine learning algorithms, including regression trees, random forests, and neural networks—from feedforward architectures to LSTMs and transformer networks for capturing dynamic and sequential data.

Throughout the course, students will apply these techniques using real datasets, with a focus on interpretability, performance, and practical implementation. This course is designed for students preparing for careers in data science, applied machine learning, quantitative research, or analytics-intensive roles in industry, government, and beyond.

This course extends the analytical tools developed in AEDS 6200 and 6210 to evaluate the general equilibrium effects of macroeconomic policy interventions. Topics include monetary policy instruments such as interest rate adjustments, forward guidance, and large-scale asset purchases, as well as fiscal interventions like tax reforms, transfer programs, and countercyclical government spending. Emphasis is placed on identifying causal channels and quantifying policy impacts using both fully-specified structural dynamic stochastic general equilibrium (DSGE) models and reduced-form or semi-structural vector autoregressions (SVARs). The course emphasizes empirical calibration, estimation, and simulation-based policy evaluation.

This course examines climate change through the lens of empirical economic analysis, integrating tools from applied microeconometrics, macroeconomics, and policy evaluation. Core topics include the bidirectional relationship between economic activity and climate outcomes, estimation of climate damages, and the economic valuation of mitigation and adaptation strategies. Students will engage with empirical representations of integrated assessment models (IAMs), examine sectoral case studies (e.g., energy, agriculture, finance), and apply econometric techniques to assess ESG metrics, climate risk stress testing, and insurance-based risk transfer. Datasets and estimation methods relevant to both public policy and private-sector decision-making are featured throughout.

This course presents a rigorous framework for estimating causal effects in observational and experimental settings using the potential outcomes model. The curriculum covers treatment effect heterogeneity, average treatment effects, and related estimands. Methodological coverage includes experimental design (RCTs), quasi-experimental methods (difference-in-differences, instrumental variables, regression discontinuity), and modern machine learning approaches for causal inference (e.g., causal forests, double machine learning). Students will use statistical software to replicate and extend empirical studies in applied fields such as labor economics, health policy, and environmental economics. Emphasis is placed on research design, identification strategies, and robustness analysis.

This course develops applied forecasting techniques for time series and high-dimensional panel data environments. Students will learn classical univariate and multivariate models (ARIMA, VAR), nonlinear extensions, and forecast combination methods. Coverage includes point, interval, and density forecasting, forecast evaluation under loss functions, and forecast uncertainty quantification. The course then advances to big-data settings, including penalized regression (e.g., LASSO), factor models, and machine learning algorithms suitable for structured and unstructured data. Applications include macroeconomic nowcasting, financial risk modeling, and natural language-based forecasting. Students will work hands-on with large-scale datasets and implement models in a programming environment.

This course uses tools from modern industrial organization to analyze firm behavior, strategic interaction, and market structure, with a focus on digital platforms and online marketplaces. Topics include demand estimation, pricing strategies, entry and platform competition, network effects, and algorithmic matching. Students will also explore the design and empirical evaluation of allocation mechanisms including auctions and centralized matching algorithms (e.g., Gale-Shapley). The course blends theoretical models (e.g., discrete choice, game-theoretic models) with structural estimation and reduced-form empirical techniques, preparing students to engage with policy questions in antitrust, regulation, and platform governance.

In this culminating course, students conduct applied research on a policy-relevant or business-focused problem using the full empirical and modeling toolkit acquired in the program. Working in teams, students will design and execute an end-to-end project—formulating a research question, acquiring and cleaning data, selecting appropriate methodologies, estimating models, and delivering a final written report and oral presentation.

Project options will be drawn from real-world challenges faced by partner organizations across public and private sectors. Each case will include guiding questions, suggested data sources, and recommended modeling strategies. Custom projects may be approved by the instructor. The course emphasizes empirical rigor, research transparency, and the clear communication of economic insights to both technical and non-technical audiences.